SoroushVahidi/scidocs huggingface dataset:
A Supervised Learning Framework for Arbitrary Lagrangian-Eulerian Simulations
(2016)

The Arbitrary Lagrangian-Eulerian (ALE) method is used in a variety of engineering and scientific applications for enabling multi-physics simulations. Unfortunately, the ALE method can suffer from simulation failures that require users to adjust parameters iteratively in order to complete a simulation. In this paper, we present a supervised learning framework for predicting conditions leading to simulation failures. To our knowledge, this is the first time machine learning has been applied to ALE simulations. We propose a novel learning representation for mapping the ALE domain onto a supervised learning formulation. We analyze the predictability of these failures and evaluate our framework using well-known test problems.
A Compact Multipath Mitigating Ground Plane for Multiband GNSS Antennas
(2013)

This paper presents the design of a novel multipath mitigating ground plane for global navigation satellite system (GNSS) antennas. First, the concept of a compact low multipath cross-plate reflector ground plane (CPRGP) is presented. In comparison with the choke ring and electromagnetic band gap (EBG) ground planes, the proposed CPRGP has compact size, low mass, wide operational bandwidth, and simple configuration. The proposed CPRGP is then integrated with a circularly polarized dual-band GNSS antenna in order to assess the multipath mitigating performance over two frequency bands. Measurement results of the proposed CPRGP with GNSS antenna achieves a front-to-back ratio (FBR) over 25 dB at L1 (1.575 GHz) and L2 (1.227 GHz) bands and maximum backward cross-polarization levels below -23 dB at both bands. Antenna phase center variation remains less than 2 mm across both L1 and L2 bands. Furthermore, the performance comparison of the proposed CPRGP with the commercially available pinwheel antenna and the shallow corrugated ground plane is presented, showing the advantages of CPRGP for high precision GNSS applications.
Emotion regulation through listening to music in everyday situations.
(2012)

Music is a stimulus capable of triggering an array of basic and complex emotions. We investigated whether and how individuals employ music to induce specific emotional states in everyday situations for the purpose of emotion regulation. Furthermore, we wanted to examine whether specific emotion-regulation styles influence music selection in specific situations. Participants indicated how likely it would be that they would want to listen to various pieces of music (which are known to elicit specific emotions) in various emotional situations. Data analyses by means of non-metric multidimensional scaling revealed a clear preference for pieces of music that were emotionally congruent with an emotional situation. In addition, we found that specific emotion-regulation styles might influence the selection of pieces of music characterised by specific emotions. Our findings demonstrate emotion-congruent music selection and highlight the important role of specific emotion-regulation styles in the selection of music in everyday situations.
Artificial intelligence in medicine and cardiac imaging: harnessing big data and advanced computing to provide personalized medical diagnosis and treatment.
(2014)

Although advances in information technology in the past decade have come in quantum leaps in nearly every aspect of our lives, they seem to be coming at a slower pace in the field of medicine. However, the implementation of electronic health records (EHR) in hospitals is increasing rapidly, accelerated by the meaningful use initiatives associated with the Center for Medicare & Medicaid Services EHR Incentive Programs. The transition to electronic medical records and availability of patient data has been associated with increases in the volume and complexity of patient information, as well as an increase in medical alerts, with resulting "alert fatigue" and increased expectations for rapid and accurate diagnosis and treatment. Unfortunately, these increased demands on health care providers create greater risk for diagnostic and therapeutic errors. In the near future, artificial intelligence (AI)/machine learning will likely assist physicians with differential diagnosis of disease, treatment options suggestions, and recommendations, and, in the case of medical imaging, with cues in image interpretation. Mining and advanced analysis of "big data" in health care provide the potential not only to perform "in silico" research but also to provide "real time" diagnostic and (potentially) therapeutic recommendations based on empirical data. "On demand" access to high-performance computing and large health care databases will support and sustain our ability to achieve personalized medicine. The IBM Jeopardy! Challenge, which pitted the best all-time human players against the Watson computer, captured the imagination of millions of people across the world and demonstrated the potential to apply AI approaches to a wide variety of subject matter, including medicine. The combination of AI, big data, and massively parallel computing offers the potential to create a revolutionary way of practicing evidence-based, personalized medicine.
Arabic language sentiment analysis on health services
(2017)

The social media network phenomenon creates massive amounts of valuable data that is available online and easy to access. Many users share images, videos, comments, reviews, news and opinions on different social networks sites, with Twitter being one of the most popular ones. Data collected from Twitter is highly unstructured, and extracting useful information from tweets is a challenging task. Twitter has a huge number of Arabic users who mostly post and write their tweets using the Arabic language. While there has been a lot of research on sentiment analysis in English, the amount of researches and datasets in Arabic language is limited. This paper introduces an Arabic language dataset, which is about opinions on health services and has been collected from Twitter. The paper will first detail the process of collecting the data from Twitter and also the process of filtering, pre-processing and annotating the Arabic text in order to build a big sentiment analysis dataset in Arabic. Several Machine Learning algorithms (Naïve Bayes, Support Vector Machine and Logistic Regression) alongside Deep and Convolutional Neural Networks were utilized in our experiments of sentiment analysis on our health dataset.
Neck lift my way: an update.
(2014)

BACKGROUND
The author updates prior descriptions of an approach to the surgical neck lift that aims for a maximum degree of control over the size, shape, and position of every anatomical feature of the neck that is negatively affecting its appearance.


METHODS
A 38-year clinical experience guided the development of the operative tactics that define the strategy. Data collected from a records review of 522 consecutive neck lifts performed during the 10-year period 2004 through 2013 further inform the report. The approach has eight features: (1) nearly routine use of open submental access to all tissue layers of the central neck, including a regimen that curbed the problems that may attend an extensive tissue dissection; (2) management of lax neck skin by lateral excision using a specific postauricular incision, or by using the nonexcisional method of redistribution; (3) open lipectomy for precise removal of excess subcutaneous neck and jawline fat; (4) individualized modifications to subplatysmal fat, perihyoid fascia, and anterior digastric muscles; (5) treatment of large, ptotic, or malpositioned submandibular salivary glands by partial excision using a transcutaneous traction suture; (6) the current version of the corset platysmaplasty, which is used to treat static paramedian platysma muscle bands, and to avoid contour imperfections following subplatysmal maneuvers; (7) an approach that facilitates an isolated neck lift; and (8) durable results.


RESULTS
Case examples demonstrate outcomes.


CONCLUSION
Although the updated approach remains relatively complex and invasive, the author believes that the ends justify the means.
Emulation of Software Defined Networks Using Mininet in Different Simulation Environments
(2015)

In this paper an evaluation of an SDN emulation tool called Mininet is conducted. Tests were conducted to study Mininnet limitations related to the simulation environment, resource capabilities. To evaluate the later, the scalability of Mininet in term of creating many topologies is tested with varying number of nodes and two different environment scenarios. Results show that the simulation environment has a remarkable effect on the required time for building a topology, for instance, the powerful resources scenario needed only 0.19 sec, whereas, 5.611 sec were needed when the resources were less. However, the required time were increased in both scenarios when the number of nodes was increased into 242.842 and 3718.117 sec for the powerful and less capabilities resources respectively.
Identifying Women's Experiences With and Strategies for Mitigating Negative Effects of Online Harassment
(2017)

The popularity, availability, and ubiquity of information and communication technologies create new opportunities for online harassment. The present study evaluates factors associated with young adult women's online harassment experiences through a multi-factor measure accounting for the frequency and severity of negative events. Findings from a survey of 659 undergraduate and graduate students highlight the relationship between harassment, well-being, and engagement in strategies to manage one's online identity. We further identify differences in harassment experiences across three popular social media platforms: Facebook, Twitter, and Instagram. We conclude by discussing this study's contribution to feminist theory and describing five potential design interventions derived from our data that may minimize these negative experiences, mitigate the psychological harm they cause, and provide women with more proactive ways to regain agency when using communication technologies.
Silhouette Smoothing for Real-Time Rendering of Mesh Surfaces
(2008)

Coarse piecewise linear approximation of surfaces causes the undesirable polygonal appearance of silhouettes. We present an efficient method for smoothing the silhouettes of coarse triangle meshes using efficient 3D curve reconstruction and simple local remeshing. It does not assume the availability of a fine mesh and generates only a moderate amount of additional data at runtime. Furthermore, polygonal feature edges are also smoothed in a unified framework. Our method is based on a novel interpolation scheme over silhouette triangles, and this ensures that smooth silhouettes are faithfully reconstructed and always change continuously with respect to the continuous movement of the viewpoint or objects. We speed up computation with GPU assistance to achieve real-time rendering of coarse meshes with the smoothed silhouettes. Experiments show that this method outperforms previous methods for silhouette smoothing.
Robustness Analysis of Artificial Neural Networks and Support Vector Machine in Making Prediction
(2011)

This This study aims to investigate the robustness of prediction model by comparing artificial neural networks (ANNs), and support vector machine (SVMs) model. The study employs ten years monthly data of six types of macroeconomic variables as independent variables and the average rate of return of one-month time deposit of Indonesian Islamic banks (RR) as dependent variable. Finally, the performance is evaluated through graph analysis, statistical parameters and accuracy rate measurement. This research found that ANNs outperforms SVMs empirically resulted from the training process and overall data prediction. This is indicating that ANNs model is better in the context of capturing all data pattern and explaining the volatility of RR.
Biofeedback-based behavioral treatment for chronic tinnitus: results of a randomized controlled trial.
(2008)

Many tinnitus sufferers believe that their tinnitus has an organic basis and thus seek medical rather than psychological treatments. Tinnitus has been found to be associated with negative appraisal, dysfunctional attention shift, and heightened psychophysiological arousal, so cognitive-behavioral interventions and biofeedback are commonly suggested as treatments. This study developed and investigated the efficacy of a biofeedback-based cognitive-behavioral treatment for tinnitus. In total, 130 tinnitus patients were randomly assigned to an intervention or a wait-list control group. Treatment consisted of 12 sessions of a biofeedback-based behavioral intervention over a 3-month period. Patients in the wait-list group participated in the treatment after the intervention group had completed the treatment. Results showed clear improvements regarding tinnitus annoyance, diary ratings of loudness, and feelings of controllability. Furthermore, changes in coping cognitions as well as changes in depressive symptoms were found. Improvements were maintained over a 6-month follow-up period in which medium-to-large effect sizes were observed. The treatment developed and investigated in this study is well accepted and leads to clear and stable improvements. Through demonstration of psychophysiological interrelationships, the treatment enables patients to change their somatic illness perceptions to a more psychosomatic point of view.
Lung nodule detection in CT images using deep convolutional neural networks
(2016)

Early detection of lung nodules in thoracic Computed Tomography (CT) scans is of great importance for the successful diagnosis and treatment of lung cancer. Due to improvements in screening technologies, and an increased demand for their use, radiologists are required to analyze an ever increasing amount of image data, which can affect the quality of their diagnoses. Computer-Aided Detection (CADe) systems are designed to assist radiologists in this endeavor. Here, we present a CADe system for the detection of lung nodules in thoracic CT images. Our system is based on (1) the publicly available Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI) database, which contains 1018 thoracic CT scans with nodules of different shape and size, and (2) a deep Convolutional Neural Network (CNN), which is trained, using the back-propagation algorithm, to extract valuable volumetric features from the input data and detect lung nodules in sub-volumes of CT images. Considering only those test nodules that have been annotated by four radiologists, our CADe system achieves a sensitivity (true positive rate) of 78.9% with 20 false positives (FPs) per scan, or a sensitivity of 71.2% with 10 FPs per scan. This is achieved without using any segmentation or additional FP reduction procedures, both of which are commonly used in other CADe systems. Furthermore, our CADe system is validated on a larger number of lung nodules compared to other studies, which increases the variation in their appearance, and therefore, makes their detection by a CADe system more challenging.
MAPGEN: Mixed-Initiative Planning and Scheduling for the Mars Exploration Rover Mission
(2004)

8 1094-7167/04/$20.00 © 2004 IEEE IEEE INTELLIGENT SYSTEMS to elucidate the planet’s past climate, water activity, and habitability. Science is MER’s primary driver, so making best use of the scientific instruments, within the available resources, is a crucial aspect of the mission. To address this criticality, the MER project team selected MAPGEN (Mixed Initiative Activity Plan Generator) as an activity-planning tool. MAPGEN combines two existing systems, each with a strong heritage: the APGEN activity-planning tool1 from the Jet Propulsion Laboratory and the Europa planning and scheduling system2 from NASA Ames Research Center. This article discusses the issues arising from combining these tools in this mission’s context.
Robot control design using virtual path from android smartphone
(2017)

Progress robot development very varied almost all fields seize the attention of the world's scientists and researchers. For some cases, robots are generally used to help humans in relieving job. The robot can replace human mobilization for move so that the efficiency of wasted time will be less. Many things need to be note what kind of robot control reliable to replace human work in mobilization. Virtual world as in android smartphone greatly benefit humans to creating control tools easy to use anywhere and could represent a desire humans. By making control robot using virtual paths on the Android smartphone which have an accuracy of more than 70% to a scale of 1:50 with the actual track in an environment that is not wide enough, the advantage can be used to control the robot move easily and efficiently compared to the usual manual control robot that will take more time to control The robot moves its place as desired.
3D Human Pose Estimation in RGBD Images for Robotic Task Learning
(2018)

We propose an approach to estimate 3D human pose in real world units from a single RGBD image and show that it exceeds performance of monocular 3D pose estimation approaches from color as well as pose estimation exclusively from depth. Our approach builds on robust human keypoint detectors for color images and incorporates depth for lifting into 3D. We combine the system with our learning from demonstration framework to instruct a service robot without the need of markers. Experiments in real world settings demonstrate that our approach enables a PR2 robot to imitate manipulation actions observed from a human teacher.
The Levels of Conceptual Interoperability Model
(2003)

Interoperability of systems is not a cookie-cutter-function. There are various levels of interoperability between two systems ranging from no interoperability to full interoperability. In the technical domain, various models for levels of interoperability already exist and are used successfully to determine the degree of interoperability between information technology systems. However, such models are not yet established in the domain of conceptual modeling. This paper introduces a general model dealing with various levels of conceptual interoperability that goes beyond the technical reference models for interoperable solutions. The model is intended to become a bridge between the conceptual design and the technical design for implementation, integration, or federation. It should also contribute to the standardization of V&V procedures as well as to the documentation of systems that are designed to be federated. It is furthermore a framework to determine in the early stages of the federation development process whether meaningful interoperability between systems is possible. To this end, the scope of the model goes beyond the implementation level of actual standards, which focus on the exchange of data using standardized formats and interfaces. Another practical application of the model is, that it enhances the only recently published DoD Net-Centric Data Strategy for the Global Information Grid (GIG) and is directly applicable to derive necessary metadata to reach the DoD Data Goal to “enable Data to be understandable.”
Electronic Data Interchange and Small Organizations: Adoption and Impact of Technology
(1995)

Many EDI researchers and practitioners have recognized the importance of high penetration levels for the success of EDI. Unfortunately, such penetration is partly impeded by the resistance of small companies to become EDI capable. To investigate this issue, three major factors are identified that influence the EDI adoption practices of small firms. These factors are: organizational readiness (because of the low levels of IT sophistication and resource availability of small firms), external pressures to adopt (because of the weak market positions of small firms and the network nature of the technology), and perceived benefits (because of the limited impact that IT has on small firms due to under-utilization and lack of integration). By combining the anticipated effects of these factom, we developed a framework of EDI adoption by small businesses. The applicability of this framework is empirically demonstrated using the results of seven case studies. Finally, recommendations are made for the development of successful EDI partner expansion plans. These include the development of a long-term EDI partner expansion plan from the very beginning, the individual assessment of each partner’s EDI preparedness level, and the selection of appropriate influence tactics to expedite adoption by small partners. Specifically, it is suggested that EDI initiators pursue promotional efforts to improve partners’ perceptions of EDI benefits, provide financial and technological assistance to partners with low organizational readiness, and carefully select and enact influence strategies to reduce resistance.
The Modeling Process for Stage Models
(2010)

The purpose of this paper is to present and test a modeling procedure because researchers have struggled for decades to develop stages-of-growth models that are both theoretically founded and empirically validated. This article presents the concept and hypothesis of stages, the history of stage models, and a procedure that may serve as a useful tool in modeling stages of growth. Based on previous research and lessons learned from case study experience of the government sector in Norway, a procedure for the stages of growth modeling process is suggested and demonstrated. The procedure is used for developing a stage model for e-government interoperability. This article provides new insight into issues and challenges faced when engaging in stages of growth research. The paper proposes a new approach to stages of growth modeling. The utility of the suggested procedure is to improve theory building and empirical validation. The contribution to academia is the modeling process that can be applied in future developments of stages of growth. The contribution to practice lies in the stage hypothesis of organizational development over time.
The Case Research Strategy in Studies of Information Systems
(1987)

This article defines and discusses one of these qualitative methods--the case research strategy. Suggestions are provided for researchers who wish to undertake research employing this approach. Criteria for the evaluation of case research are established and several characteristics useful for categorizing the studies are identified. A sample of papers drawn from information systems journals is reviewed. The paper concludes with examples of research areas that are particularly wellsuited to investigation using the case research approach. ACM categories: H.O., J.O.